Abstract
Computer games are gaining popularity by every passing day. This has increased the number of choices in computer games for the users. At the same time the quality of entertainment provided by these games has also decreased due to abundance of games in the market for personal computers. On the other hand the task of game development for the developers is becoming tiresome, which requires scripting the game, modeling its contents and other such activities. Still it cannot be known how much the developed game is entertaining for the end users. As entertainment is a subjective term. What might be entertaining for one user may not be entertaining for others. Another issue from the point of view of game developers is the constant need of writing new games, requiring investment both in terms of time and resources. In this work we create a set of metrics for measuring entertainment in computer games. The genres we address are board based games and predator/prey type of games. The metrics devised are based on different theories of entertainment specifically related to computer games, taken from literature. Further we use Evolutionary Algorithm (EA) to generate new and entertaining games using the proposed entertainment metrics as the fitness function. The EA starts with a randomly initialized set of population and using genetic operators (guided by the proposed entertainment metrics) we reach a final set of population that is optimized against entertainment. For the purpose of verifying the entertainment value of the evolved games with that of the human we conduct a human user survey and experiment using the controller learning ability.
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References
Olson, C.K., Kutner, L.A., Warner, D.E., Almerigi, J.B., Baer, L., Nicholi, A.M., Beresin, E.V.: Factors correlated with violent video game use by adolescent boys and girls. Journal of Adolescent Health, 77–83 (2007)
Schmidhuber, J.: Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science 18, 173–187 (2006)
Iida, H., Takeshita, N., Yoshimura, J.: A Metric for Entertainment of Board Games: Its Application for Evolution of Chess Variants. In: Nakatsu, R., Hoshino, J. (eds.) Entertainment Computing: Technologies and Applications Proceedings of IWEC 2002 , pp. 65–72. Kluwer Academic Publishers, Boston (2003)
Cincotti, A., Iida, H.: Outcome Uncertainty And Interestedness In Game-Playing: A Case Study Using Synchronized Hex. In: New Mathematics and Natural Computation (NMNC), vol. 02(02), pp. 173–181 (2006)
Retalis, S.: Creating Adaptive e-Learning Board Games for School Settings Using the ELG Environment. Journal of Universal Computer Science, 2897–2908 (2008)
Togelius, J., Nardi, R.D., Lucas, S.M.: Towards automatic personalised content creation for racing games. In: Proceedings of the IEEE Symposium on Computational Intelligence and Games, Piscataway, NJ, April 1-5 (2007)
Yannakakis, G.N., Hallam, J.: Towards Optimizing Entertainment In Computer Games. Applied Artificial Intelligence 21(10), 933–971 (2007)
Gallagher, M., Ryan, A.: Learning to Play Pac-Man: An Evolutionary Rule-based Approach. In: Proceedings of IEEE Congress on Evolutionary Computation, Canberra, Australia, December 8-12 (2003)
Lankveld, G., Spronck, P., Rauterberg, M.: Difficulty Scaling through Incongruity. In: Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, Stanford, California, October 22-24 (2008)
Togelius, J., Schmidhuber, J.: An Experiment in Automatic Game Design. In: Proceedings of IEEE Computational Intelligence and Games, Perth, Australia, December 15-18 (2008)
Pedersen, C., Togelius, J., Yannakakis, G.N.: Optimization of platform game levels for player experience. In: Proceedings of Artificial Intelligence and Interactive Digital Entertainment, Stanford, California, October 14-16 (2009)
Compton, K., Mateas, M.: Procedural Level Design for Platform Games. In: Proceedings of 2nd Artificial Intelligence and Interactive Digital Entertainment Conference, Stanford, California, June 20-23 (2006)
Yannakakis, G.N., Hallam, J.: Evolving Opponents for Interesting Interactive Computer Games. In: Proceedings of the 8th International Conference on the Simulation of Adaptive Behavior, Los Angeles, USA, July 13-17 (2004)
Csikszentmihalyi, M.: Flow.: The Psychology of Optimal Experience. Harper & Row, New York (1990)
Csikszentmihalyi, M., Csikszentmihalyi, I.: Introduction to Part IV in Optimal Experience: Psychological Studies of Flow in Consciousness. Cambridge University Press, Cambridge (1988)
Malone, T.W.: What makes computer games fun? Byte 6, 258–277 (1981)
Koster, R.: A Theory of Fun for Game Design. Paraglyph Press (2005)
Rauterberg, M.: Amme: An Automatic Mental Model Evaluation to Analyze User Behavior Traced in a Finite, Discrete State Space. In: Proceedings of the Annual Conference of the European Association of Cognitive Ergonomics, EACE 2005, Chania, Crete, September 29-October 1 (2005)
Rauterberg, M.: About a Framework for Information and Information Processing of Learning Systems. In: Falkenberg, E., Hesse, W., Olibve, A. (eds.) Information System Concepts, pp. 54–69. IFIP Chapman & Hall, Boca Raton (1995)
Yannakakis, G.N.: How to Model and Augment Player Satisfaction: A Review. In: Proceedings of the 1st Workshop on Child, Computer and Interaction, ICMI 2008, Chania, Crete (October 2008)
Chellapilla, K., Fogel, D.B.: Evolving an expert checkers playing program without using human expertise. IEEE Trans. Evolutionary Computation 5(4), 422–428 (2001)
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Halim, Z., Baig, A.R. (2011). Evolutionary Algorithms towards Generating Entertaining Games. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_15
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DOI: https://doi.org/10.1007/978-3-642-20344-2_15
Publisher Name: Springer, Berlin, Heidelberg
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